Overview

Dataset statistics

Number of variables42
Number of observations125973
Missing cells0
Missing cells (%)0.0%
Duplicate rows8
Duplicate rows (%)< 0.1%
Total size in memory40.4 MiB
Average record size in memory336.0 B

Variable types

Numeric28
Categorical14

Alerts

num_outbound_cmds has constant value "0.0" Constant
Dataset has 8 (< 0.1%) duplicate rowsDuplicates
service has a high cardinality: 70 distinct values High cardinality
src_bytes is highly correlated with dst_bytes and 10 other fieldsHigh correlation
dst_bytes is highly correlated with src_bytes and 8 other fieldsHigh correlation
hot is highly correlated with num_compromisedHigh correlation
num_compromised is highly correlated with hotHigh correlation
root_shell is highly correlated with su_attemptedHigh correlation
su_attempted is highly correlated with root_shellHigh correlation
count is highly correlated with src_bytes and 10 other fieldsHigh correlation
srv_count is highly correlated with countHigh correlation
serror_rate is highly correlated with src_bytes and 9 other fieldsHigh correlation
srv_serror_rate is highly correlated with src_bytes and 8 other fieldsHigh correlation
rerror_rate is highly correlated with srv_rerror_rate and 2 other fieldsHigh correlation
srv_rerror_rate is highly correlated with rerror_rate and 2 other fieldsHigh correlation
same_srv_rate is highly correlated with src_bytes and 12 other fieldsHigh correlation
diff_srv_rate is highly correlated with src_bytes and 11 other fieldsHigh correlation
dst_host_count is highly correlated with count and 5 other fieldsHigh correlation
dst_host_srv_count is highly correlated with src_bytes and 7 other fieldsHigh correlation
dst_host_same_srv_rate is highly correlated with src_bytes and 11 other fieldsHigh correlation
dst_host_diff_srv_rate is highly correlated with src_bytes and 6 other fieldsHigh correlation
dst_host_same_src_port_rate is highly correlated with count and 3 other fieldsHigh correlation
dst_host_srv_diff_host_rate is highly correlated with count and 3 other fieldsHigh correlation
dst_host_serror_rate is highly correlated with src_bytes and 10 other fieldsHigh correlation
dst_host_srv_serror_rate is highly correlated with src_bytes and 7 other fieldsHigh correlation
dst_host_rerror_rate is highly correlated with rerror_rate and 2 other fieldsHigh correlation
dst_host_srv_rerror_rate is highly correlated with rerror_rate and 2 other fieldsHigh correlation
num_compromised is highly correlated with num_rootHigh correlation
root_shell is highly correlated with su_attemptedHigh correlation
su_attempted is highly correlated with root_shell and 1 other fieldsHigh correlation
num_root is highly correlated with num_compromisedHigh correlation
num_access_files is highly correlated with su_attemptedHigh correlation
count is highly correlated with same_srv_rateHigh correlation
serror_rate is highly correlated with srv_serror_rate and 5 other fieldsHigh correlation
srv_serror_rate is highly correlated with serror_rate and 5 other fieldsHigh correlation
rerror_rate is highly correlated with srv_rerror_rate and 2 other fieldsHigh correlation
srv_rerror_rate is highly correlated with rerror_rate and 2 other fieldsHigh correlation
same_srv_rate is highly correlated with count and 7 other fieldsHigh correlation
dst_host_count is highly correlated with same_srv_rate and 1 other fieldsHigh correlation
dst_host_srv_count is highly correlated with serror_rate and 5 other fieldsHigh correlation
dst_host_same_srv_rate is highly correlated with serror_rate and 6 other fieldsHigh correlation
dst_host_serror_rate is highly correlated with serror_rate and 5 other fieldsHigh correlation
dst_host_srv_serror_rate is highly correlated with serror_rate and 5 other fieldsHigh correlation
dst_host_rerror_rate is highly correlated with rerror_rate and 2 other fieldsHigh correlation
dst_host_srv_rerror_rate is highly correlated with rerror_rate and 2 other fieldsHigh correlation
src_bytes is highly correlated with dst_bytes and 6 other fieldsHigh correlation
dst_bytes is highly correlated with src_bytes and 5 other fieldsHigh correlation
hot is highly correlated with num_compromisedHigh correlation
num_compromised is highly correlated with hotHigh correlation
root_shell is highly correlated with su_attemptedHigh correlation
su_attempted is highly correlated with root_shellHigh correlation
count is highly correlated with same_srv_rateHigh correlation
serror_rate is highly correlated with src_bytes and 5 other fieldsHigh correlation
srv_serror_rate is highly correlated with src_bytes and 5 other fieldsHigh correlation
rerror_rate is highly correlated with srv_rerror_rate and 2 other fieldsHigh correlation
srv_rerror_rate is highly correlated with rerror_rate and 2 other fieldsHigh correlation
same_srv_rate is highly correlated with src_bytes and 10 other fieldsHigh correlation
diff_srv_rate is highly correlated with src_bytes and 9 other fieldsHigh correlation
dst_host_count is highly correlated with dst_host_same_src_port_rate and 1 other fieldsHigh correlation
dst_host_srv_count is highly correlated with dst_bytes and 4 other fieldsHigh correlation
dst_host_same_srv_rate is highly correlated with dst_bytes and 4 other fieldsHigh correlation
dst_host_diff_srv_rate is highly correlated with dst_bytes and 4 other fieldsHigh correlation
dst_host_same_src_port_rate is highly correlated with dst_host_countHigh correlation
dst_host_srv_diff_host_rate is highly correlated with dst_host_countHigh correlation
dst_host_serror_rate is highly correlated with src_bytes and 5 other fieldsHigh correlation
dst_host_srv_serror_rate is highly correlated with src_bytes and 5 other fieldsHigh correlation
dst_host_rerror_rate is highly correlated with rerror_rate and 2 other fieldsHigh correlation
dst_host_srv_rerror_rate is highly correlated with rerror_rate and 2 other fieldsHigh correlation
is_guest_login is highly correlated with num_outbound_cmds and 1 other fieldsHigh correlation
protocol_type is highly correlated with num_outbound_cmds and 1 other fieldsHigh correlation
num_outbound_cmds is highly correlated with is_guest_login and 12 other fieldsHigh correlation
wrong_fragment is highly correlated with num_outbound_cmdsHigh correlation
land is highly correlated with num_outbound_cmdsHigh correlation
class is highly correlated with num_outbound_cmds and 3 other fieldsHigh correlation
num_shells is highly correlated with num_outbound_cmdsHigh correlation
su_attempted is highly correlated with num_outbound_cmds and 1 other fieldsHigh correlation
service is highly correlated with is_guest_login and 4 other fieldsHigh correlation
flag is highly correlated with num_outbound_cmds and 2 other fieldsHigh correlation
is_host_login is highly correlated with num_outbound_cmdsHigh correlation
logged_in is highly correlated with num_outbound_cmds and 3 other fieldsHigh correlation
root_shell is highly correlated with num_outbound_cmds and 1 other fieldsHigh correlation
urgent is highly correlated with num_outbound_cmdsHigh correlation
duration is highly correlated with dst_host_diff_srv_rateHigh correlation
protocol_type is highly correlated with service and 3 other fieldsHigh correlation
service is highly correlated with protocol_type and 19 other fieldsHigh correlation
flag is highly correlated with service and 14 other fieldsHigh correlation
hot is highly correlated with service and 1 other fieldsHigh correlation
logged_in is highly correlated with service and 11 other fieldsHigh correlation
num_compromised is highly correlated with num_root and 1 other fieldsHigh correlation
root_shell is highly correlated with num_access_filesHigh correlation
su_attempted is highly correlated with num_access_filesHigh correlation
num_root is highly correlated with num_compromised and 1 other fieldsHigh correlation
num_access_files is highly correlated with num_compromised and 3 other fieldsHigh correlation
is_guest_login is highly correlated with service and 1 other fieldsHigh correlation
count is highly correlated with service and 12 other fieldsHigh correlation
srv_count is highly correlated with protocol_type and 2 other fieldsHigh correlation
serror_rate is highly correlated with service and 11 other fieldsHigh correlation
srv_serror_rate is highly correlated with service and 11 other fieldsHigh correlation
rerror_rate is highly correlated with flag and 7 other fieldsHigh correlation
srv_rerror_rate is highly correlated with flag and 4 other fieldsHigh correlation
same_srv_rate is highly correlated with service and 12 other fieldsHigh correlation
diff_srv_rate is highly correlated with rerror_rate and 2 other fieldsHigh correlation
srv_diff_host_rate is highly correlated with service and 1 other fieldsHigh correlation
dst_host_count is highly correlated with service and 7 other fieldsHigh correlation
dst_host_srv_count is highly correlated with service and 11 other fieldsHigh correlation
dst_host_same_srv_rate is highly correlated with service and 12 other fieldsHigh correlation
dst_host_diff_srv_rate is highly correlated with duration and 5 other fieldsHigh correlation
dst_host_same_src_port_rate is highly correlated with protocol_type and 6 other fieldsHigh correlation
dst_host_srv_diff_host_rate is highly correlated with protocol_type and 4 other fieldsHigh correlation
dst_host_serror_rate is highly correlated with service and 12 other fieldsHigh correlation
dst_host_srv_serror_rate is highly correlated with service and 10 other fieldsHigh correlation
dst_host_rerror_rate is highly correlated with flag and 5 other fieldsHigh correlation
dst_host_srv_rerror_rate is highly correlated with flag and 3 other fieldsHigh correlation
class is highly correlated with service and 11 other fieldsHigh correlation
src_bytes is highly skewed (γ1 = 190.669347) Skewed
dst_bytes is highly skewed (γ1 = 290.0529108) Skewed
num_failed_logins is highly skewed (γ1 = 53.7644243) Skewed
num_compromised is highly skewed (γ1 = 250.1078834) Skewed
num_root is highly skewed (γ1 = 236.913724) Skewed
num_file_creations is highly skewed (γ1 = 55.66534083) Skewed
num_access_files is highly skewed (γ1 = 45.55496112) Skewed
duration has 115955 (92.0%) zeros Zeros
src_bytes has 49392 (39.2%) zeros Zeros
dst_bytes has 67967 (54.0%) zeros Zeros
hot has 123302 (97.9%) zeros Zeros
num_failed_logins has 125851 (99.9%) zeros Zeros
num_compromised has 124687 (99.0%) zeros Zeros
num_root has 125324 (99.5%) zeros Zeros
num_file_creations has 125686 (99.8%) zeros Zeros
num_access_files has 125602 (99.7%) zeros Zeros
serror_rate has 86829 (68.9%) zeros Zeros
srv_serror_rate has 88754 (70.5%) zeros Zeros
rerror_rate has 109783 (87.1%) zeros Zeros
srv_rerror_rate has 109767 (87.1%) zeros Zeros
same_srv_rate has 2766 (2.2%) zeros Zeros
diff_srv_rate has 76217 (60.5%) zeros Zeros
srv_diff_host_rate has 97574 (77.5%) zeros Zeros
dst_host_same_srv_rate has 6927 (5.5%) zeros Zeros
dst_host_diff_srv_rate has 46989 (37.3%) zeros Zeros
dst_host_same_src_port_rate has 63023 (50.0%) zeros Zeros
dst_host_srv_diff_host_rate has 86904 (69.0%) zeros Zeros
dst_host_serror_rate has 81386 (64.6%) zeros Zeros
dst_host_srv_serror_rate has 85360 (67.8%) zeros Zeros
dst_host_rerror_rate has 103178 (81.9%) zeros Zeros
dst_host_srv_rerror_rate has 106616 (84.6%) zeros Zeros

Reproduction

Analysis started2023-02-20 03:06:43.624502
Analysis finished2023-02-20 03:08:52.983094
Duration2 minutes and 9.36 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

duration
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2981
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean287.14465
Minimum0
Maximum42908
Zeros115955
Zeros (%)92.0%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-02-19T21:08:53.048605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum42908
Range42908
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2604.51531
Coefficient of variation (CV)9.070394693
Kurtosis156.0768098
Mean287.14465
Median Absolute Deviation (MAD)0
Skewness11.88022985
Sum36172473
Variance6783499.999
MonotonicityNot monotonic
2023-02-19T21:08:53.154624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0115955
92.0%
11989
 
1.6%
2843
 
0.7%
3557
 
0.4%
4351
 
0.3%
5298
 
0.2%
27197
 
0.2%
6193
 
0.2%
28181
 
0.1%
7127
 
0.1%
Other values (2971)5282
 
4.2%
ValueCountFrequency (%)
0115955
92.0%
11989
 
1.6%
2843
 
0.7%
3557
 
0.4%
4351
 
0.3%
5298
 
0.2%
6193
 
0.2%
7127
 
0.1%
898
 
0.1%
995
 
0.1%
ValueCountFrequency (%)
429081
< 0.1%
428881
< 0.1%
428621
< 0.1%
428371
< 0.1%
428041
< 0.1%
427781
< 0.1%
427461
< 0.1%
427231
< 0.1%
426991
< 0.1%
426791
< 0.1%

protocol_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
tcp
102689 
udp
14993 
icmp
 
8291

Length

Max length4
Median length3
Mean length3.065815691
Min length3

Characters and Unicode

Total characters386210
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtcp
2nd rowudp
3rd rowtcp
4th rowtcp
5th rowtcp

Common Values

ValueCountFrequency (%)
tcp102689
81.5%
udp14993
 
11.9%
icmp8291
 
6.6%

Length

2023-02-19T21:08:53.235138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-19T21:08:53.306536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
tcp102689
81.5%
udp14993
 
11.9%
icmp8291
 
6.6%

Most occurring characters

ValueCountFrequency (%)
p125973
32.6%
c110980
28.7%
t102689
26.6%
u14993
 
3.9%
d14993
 
3.9%
i8291
 
2.1%
m8291
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter386210
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p125973
32.6%
c110980
28.7%
t102689
26.6%
u14993
 
3.9%
d14993
 
3.9%
i8291
 
2.1%
m8291
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Latin386210
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
p125973
32.6%
c110980
28.7%
t102689
26.6%
u14993
 
3.9%
d14993
 
3.9%
i8291
 
2.1%
m8291
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII386210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
p125973
32.6%
c110980
28.7%
t102689
26.6%
u14993
 
3.9%
d14993
 
3.9%
i8291
 
2.1%
m8291
 
2.1%

service
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct70
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
http
40338 
private
21853 
domain_u
9043 
smtp
7313 
ftp_data
6860 
Other values (65)
40566 

Length

Max length11
Median length10
Mean length5.46644916
Min length3

Characters and Unicode

Total characters688625
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowftp_data
2nd rowother
3rd rowprivate
4th rowhttp
5th rowhttp

Common Values

ValueCountFrequency (%)
http40338
32.0%
private21853
17.3%
domain_u9043
 
7.2%
smtp7313
 
5.8%
ftp_data6860
 
5.4%
eco_i4586
 
3.6%
other4359
 
3.5%
ecr_i3077
 
2.4%
telnet2353
 
1.9%
finger1767
 
1.4%
Other values (60)24424
19.4%

Length

2023-02-19T21:08:53.376049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
http40338
32.0%
private21853
17.3%
domain_u9043
 
7.2%
smtp7313
 
5.8%
ftp_data6860
 
5.4%
eco_i4586
 
3.6%
other4359
 
3.5%
ecr_i3077
 
2.4%
telnet2353
 
1.9%
finger1767
 
1.4%
Other values (60)24424
19.4%

Most occurring characters

ValueCountFrequency (%)
t145597
21.1%
p88151
12.8%
a51384
 
7.5%
h49666
 
7.2%
e49119
 
7.1%
i48525
 
7.0%
r34885
 
5.1%
_29465
 
4.3%
o24559
 
3.6%
n22585
 
3.3%
Other values (30)144689
21.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter651479
94.6%
Connector Punctuation29465
 
4.3%
Decimal Number6185
 
0.9%
Uppercase Letter1496
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t145597
22.3%
p88151
13.5%
a51384
 
7.9%
h49666
 
7.6%
e49119
 
7.5%
i48525
 
7.4%
r34885
 
5.4%
o24559
 
3.8%
n22585
 
3.5%
v22472
 
3.4%
Other values (15)114536
17.6%
Decimal Number
ValueCountFrequency (%)
41708
27.6%
31656
26.8%
0866
14.0%
5862
13.9%
9862
13.9%
1148
 
2.4%
279
 
1.3%
83
 
< 0.1%
71
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
Z862
57.6%
C187
 
12.5%
I187
 
12.5%
R187
 
12.5%
X73
 
4.9%
Connector Punctuation
ValueCountFrequency (%)
_29465
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin652975
94.8%
Common35650
 
5.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
t145597
22.3%
p88151
13.5%
a51384
 
7.9%
h49666
 
7.6%
e49119
 
7.5%
i48525
 
7.4%
r34885
 
5.3%
o24559
 
3.8%
n22585
 
3.5%
v22472
 
3.4%
Other values (20)116032
17.8%
Common
ValueCountFrequency (%)
_29465
82.7%
41708
 
4.8%
31656
 
4.6%
0866
 
2.4%
5862
 
2.4%
9862
 
2.4%
1148
 
0.4%
279
 
0.2%
83
 
< 0.1%
71
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII688625
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t145597
21.1%
p88151
12.8%
a51384
 
7.5%
h49666
 
7.2%
e49119
 
7.1%
i48525
 
7.0%
r34885
 
5.1%
_29465
 
4.3%
o24559
 
3.6%
n22585
 
3.3%
Other values (30)144689
21.0%

flag
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
SF
74945 
S0
34851 
REJ
11233 
RSTR
 
2421
RSTO
 
1562
Other values (6)
 
961

Length

Max length6
Median length2
Mean length2.156041374
Min length2

Characters and Unicode

Total characters271603
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSF
2nd rowSF
3rd rowS0
4th rowSF
5th rowSF

Common Values

ValueCountFrequency (%)
SF74945
59.5%
S034851
27.7%
REJ11233
 
8.9%
RSTR2421
 
1.9%
RSTO1562
 
1.2%
S1365
 
0.3%
SH271
 
0.2%
S2127
 
0.1%
RSTOS0103
 
0.1%
S349
 
< 0.1%

Length

2023-02-19T21:08:53.454062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sf74945
59.5%
s034851
27.7%
rej11233
 
8.9%
rstr2421
 
1.9%
rsto1562
 
1.2%
s1365
 
0.3%
sh271
 
0.2%
s2127
 
0.1%
rstos0103
 
0.1%
s349
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
S114797
42.3%
F74945
27.6%
034954
 
12.9%
R17740
 
6.5%
E11233
 
4.1%
J11233
 
4.1%
T4132
 
1.5%
O1711
 
0.6%
1365
 
0.1%
H317
 
0.1%
Other values (2)176
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter236108
86.9%
Decimal Number35495
 
13.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S114797
48.6%
F74945
31.7%
R17740
 
7.5%
E11233
 
4.8%
J11233
 
4.8%
T4132
 
1.8%
O1711
 
0.7%
H317
 
0.1%
Decimal Number
ValueCountFrequency (%)
034954
98.5%
1365
 
1.0%
2127
 
0.4%
349
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin236108
86.9%
Common35495
 
13.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
S114797
48.6%
F74945
31.7%
R17740
 
7.5%
E11233
 
4.8%
J11233
 
4.8%
T4132
 
1.8%
O1711
 
0.7%
H317
 
0.1%
Common
ValueCountFrequency (%)
034954
98.5%
1365
 
1.0%
2127
 
0.4%
349
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII271603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S114797
42.3%
F74945
27.6%
034954
 
12.9%
R17740
 
6.5%
E11233
 
4.1%
J11233
 
4.1%
T4132
 
1.5%
O1711
 
0.6%
1365
 
0.1%
H317
 
0.1%
Other values (2)176
 
0.1%

src_bytes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct3341
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45566.743
Minimum0
Maximum1379963888
Zeros49392
Zeros (%)39.2%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-02-19T21:08:53.538077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median44
Q3276
95-th percentile1480
Maximum1379963888
Range1379963888
Interquartile range (IQR)276

Descriptive statistics

Standard deviation5870331.182
Coefficient of variation (CV)128.8292907
Kurtosis39354.12125
Mean45566.743
Median Absolute Deviation (MAD)44
Skewness190.669347
Sum5740179316
Variance3.446078819 × 1013
MonotonicityNot monotonic
2023-02-19T21:08:53.627592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
049392
39.2%
83691
 
2.9%
12432
 
1.9%
442334
 
1.9%
452089
 
1.7%
10322001
 
1.6%
461294
 
1.0%
431284
 
1.0%
105998
 
0.8%
147948
 
0.8%
Other values (3331)59510
47.2%
ValueCountFrequency (%)
049392
39.2%
12432
 
1.9%
42
 
< 0.1%
528
 
< 0.1%
6147
 
0.1%
7107
 
0.1%
83691
 
2.9%
9199
 
0.2%
10195
 
0.2%
1176
 
0.1%
ValueCountFrequency (%)
13799638881
< 0.1%
11675194971
< 0.1%
6933756401
< 0.1%
6215686631
< 0.1%
3817090901
< 0.1%
2172773391
< 0.1%
895815201
< 0.1%
244187761
< 0.1%
219455201
< 0.1%
188289761
< 0.1%

dst_bytes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct9326
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19779.11442
Minimum0
Maximum1309937401
Zeros67967
Zeros (%)54.0%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-02-19T21:08:53.722609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3516
95-th percentile8314
Maximum1309937401
Range1309937401
Interquartile range (IQR)516

Descriptive statistics

Standard deviation4021269.151
Coefficient of variation (CV)203.3088573
Kurtosis90941.73453
Mean19779.11442
Median Absolute Deviation (MAD)0
Skewness290.0529108
Sum2491634381
Variance1.617060559 × 1013
MonotonicityNot monotonic
2023-02-19T21:08:53.817626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
067967
54.0%
1051497
 
1.2%
8314888
 
0.7%
330528
 
0.4%
331512
 
0.4%
44511
 
0.4%
42478
 
0.4%
328470
 
0.4%
332469
 
0.4%
4454
 
0.4%
Other values (9316)52199
41.4%
ValueCountFrequency (%)
067967
54.0%
122
 
< 0.1%
31
 
< 0.1%
4454
 
0.4%
54
 
< 0.1%
61
 
< 0.1%
121
 
< 0.1%
141
 
< 0.1%
1547
 
< 0.1%
161
 
< 0.1%
ValueCountFrequency (%)
13099374011
< 0.1%
4002910602
< 0.1%
70286521
< 0.1%
51554681
< 0.1%
51537711
< 0.1%
51534601
< 0.1%
51513851
< 0.1%
51511541
< 0.1%
51510491
< 0.1%
51509381
< 0.1%

land
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0
125948 
1
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125973
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0125948
> 99.9%
125
 
< 0.1%

Length

2023-02-19T21:08:53.899640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-19T21:08:53.970652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0125948
> 99.9%
125
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0125948
> 99.9%
125
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number125973
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0125948
> 99.9%
125
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common125973
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0125948
> 99.9%
125
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII125973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0125948
> 99.9%
125
 
< 0.1%

wrong_fragment
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0.0
124883 
3.0
 
884
1.0
 
206

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters377919
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0124883
99.1%
3.0884
 
0.7%
1.0206
 
0.2%

Length

2023-02-19T21:08:54.028662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-19T21:08:54.097674image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0124883
99.1%
3.0884
 
0.7%
1.0206
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0250856
66.4%
.125973
33.3%
3884
 
0.2%
1206
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number251946
66.7%
Other Punctuation125973
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0250856
99.6%
3884
 
0.4%
1206
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.125973
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common377919
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0250856
66.4%
.125973
33.3%
3884
 
0.2%
1206
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII377919
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0250856
66.4%
.125973
33.3%
3884
 
0.2%
1206
 
0.1%

urgent
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0.0
125964 
1.0
 
5
2.0
 
3
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters377919
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0125964
> 99.9%
1.05
 
< 0.1%
2.03
 
< 0.1%
3.01
 
< 0.1%

Length

2023-02-19T21:08:54.157685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-19T21:08:54.230697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0125964
> 99.9%
1.05
 
< 0.1%
2.03
 
< 0.1%
3.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0251937
66.7%
.125973
33.3%
15
 
< 0.1%
23
 
< 0.1%
31
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number251946
66.7%
Other Punctuation125973
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0251937
> 99.9%
15
 
< 0.1%
23
 
< 0.1%
31
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.125973
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common377919
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0251937
66.7%
.125973
33.3%
15
 
< 0.1%
23
 
< 0.1%
31
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII377919
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0251937
66.7%
.125973
33.3%
15
 
< 0.1%
23
 
< 0.1%
31
 
< 0.1%

hot
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2044088813
Minimum0
Maximum77
Zeros123302
Zeros (%)97.9%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-02-19T21:08:54.297709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum77
Range77
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.149968434
Coefficient of variation (CV)10.51797955
Kurtosis168.0142645
Mean0.2044088813
Median Absolute Deviation (MAD)0
Skewness12.58988613
Sum25750
Variance4.622364266
MonotonicityNot monotonic
2023-02-19T21:08:54.373222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0123302
97.9%
21037
 
0.8%
1369
 
0.3%
28277
 
0.2%
30256
 
0.2%
4173
 
0.1%
6140
 
0.1%
576
 
0.1%
2468
 
0.1%
1957
 
< 0.1%
Other values (18)218
 
0.2%
ValueCountFrequency (%)
0123302
97.9%
1369
 
0.3%
21037
 
0.8%
354
 
< 0.1%
4173
 
0.1%
576
 
0.1%
6140
 
0.1%
75
 
< 0.1%
81
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
771
 
< 0.1%
442
 
< 0.1%
331
 
< 0.1%
30256
0.2%
28277
0.2%
252
 
< 0.1%
2468
 
0.1%
2255
 
< 0.1%
211
 
< 0.1%
209
 
< 0.1%

num_failed_logins
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.001222484183
Minimum0
Maximum5
Zeros125851
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-02-19T21:08:54.441734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.04523913898
Coefficient of variation (CV)37.00590945
Kurtosis3869.069296
Mean0.001222484183
Median Absolute Deviation (MAD)0
Skewness53.7644243
Sum154
Variance0.002046579696
MonotonicityNot monotonic
2023-02-19T21:08:54.502245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0125851
99.9%
1104
 
0.1%
29
 
< 0.1%
35
 
< 0.1%
43
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
0125851
99.9%
1104
 
0.1%
29
 
< 0.1%
35
 
< 0.1%
43
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
51
 
< 0.1%
43
 
< 0.1%
35
 
< 0.1%
29
 
< 0.1%
1104
 
0.1%
0125851
99.9%

logged_in
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0
76121 
1
49852 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125973
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
076121
60.4%
149852
39.6%

Length

2023-02-19T21:08:54.569756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-19T21:08:54.637268image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
076121
60.4%
149852
39.6%

Most occurring characters

ValueCountFrequency (%)
076121
60.4%
149852
39.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number125973
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
076121
60.4%
149852
39.6%

Most occurring scripts

ValueCountFrequency (%)
Common125973
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
076121
60.4%
149852
39.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII125973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
076121
60.4%
149852
39.6%

num_compromised
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct88
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2792503155
Minimum0
Maximum7479
Zeros124687
Zeros (%)99.0%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-02-19T21:08:54.707780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7479
Range7479
Interquartile range (IQR)0

Descriptive statistics

Standard deviation23.94204224
Coefficient of variation (CV)85.73684938
Kurtosis75956.22769
Mean0.2792503155
Median Absolute Deviation (MAD)0
Skewness250.1078834
Sum35178
Variance573.2213868
MonotonicityNot monotonic
2023-02-19T21:08:54.795296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0124687
99.0%
1976
 
0.8%
298
 
0.1%
440
 
< 0.1%
338
 
< 0.1%
619
 
< 0.1%
517
 
< 0.1%
75
 
< 0.1%
83
 
< 0.1%
93
 
< 0.1%
Other values (78)87
 
0.1%
ValueCountFrequency (%)
0124687
99.0%
1976
 
0.8%
298
 
0.1%
338
 
< 0.1%
440
 
< 0.1%
517
 
< 0.1%
619
 
< 0.1%
75
 
< 0.1%
83
 
< 0.1%
93
 
< 0.1%
ValueCountFrequency (%)
74791
< 0.1%
17391
< 0.1%
10431
< 0.1%
8842
< 0.1%
8091
< 0.1%
7891
< 0.1%
7671
< 0.1%
7611
< 0.1%
7561
< 0.1%
7511
< 0.1%

root_shell
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0.0
125804 
1.0
 
169

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters377919
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0125804
99.9%
1.0169
 
0.1%

Length

2023-02-19T21:08:54.880810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-19T21:08:54.950322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0125804
99.9%
1.0169
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0251777
66.6%
.125973
33.3%
1169
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number251946
66.7%
Other Punctuation125973
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0251777
99.9%
1169
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.125973
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common377919
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0251777
66.6%
.125973
33.3%
1169
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII377919
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0251777
66.6%
.125973
33.3%
1169
 
< 0.1%

su_attempted
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0.0
125893 
2.0
 
59
1.0
 
21

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters377919
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0125893
99.9%
2.059
 
< 0.1%
1.021
 
< 0.1%

Length

2023-02-19T21:08:55.011333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-19T21:08:55.087345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0125893
99.9%
2.059
 
< 0.1%
1.021
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0251866
66.6%
.125973
33.3%
259
 
< 0.1%
121
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number251946
66.7%
Other Punctuation125973
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0251866
> 99.9%
259
 
< 0.1%
121
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.125973
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common377919
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0251866
66.6%
.125973
33.3%
259
 
< 0.1%
121
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII377919
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0251866
66.6%
.125973
33.3%
259
 
< 0.1%
121
 
< 0.1%

num_root
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct82
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3021917395
Minimum0
Maximum7468
Zeros125324
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-02-19T21:08:55.162358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7468
Range7468
Interquartile range (IQR)0

Descriptive statistics

Standard deviation24.39961809
Coefficient of variation (CV)80.74217425
Kurtosis70070.20876
Mean0.3021917395
Median Absolute Deviation (MAD)0
Skewness236.913724
Sum38068
Variance595.3413629
MonotonicityNot monotonic
2023-02-19T21:08:55.252374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0125324
99.5%
1273
 
0.2%
9121
 
0.1%
699
 
0.1%
233
 
< 0.1%
524
 
< 0.1%
412
 
< 0.1%
37
 
< 0.1%
72
 
< 0.1%
8572
 
< 0.1%
Other values (72)76
 
0.1%
ValueCountFrequency (%)
0125324
99.5%
1273
 
0.2%
233
 
< 0.1%
37
 
< 0.1%
412
 
< 0.1%
524
 
< 0.1%
699
 
0.1%
72
 
< 0.1%
81
 
< 0.1%
9121
 
0.1%
ValueCountFrequency (%)
74681
< 0.1%
17431
< 0.1%
10451
< 0.1%
9931
< 0.1%
9751
< 0.1%
8891
< 0.1%
8671
< 0.1%
8572
< 0.1%
8491
< 0.1%
8411
< 0.1%

num_file_creations
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01266938153
Minimum0
Maximum43
Zeros125686
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-02-19T21:08:55.342390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum43
Range43
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4839350694
Coefficient of variation (CV)38.19721334
Kurtosis3603.311713
Mean0.01266938153
Median Absolute Deviation (MAD)0
Skewness55.66534083
Sum1596
Variance0.2341931514
MonotonicityNot monotonic
2023-02-19T21:08:55.420404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0125686
99.8%
1151
 
0.1%
241
 
< 0.1%
413
 
< 0.1%
35
 
< 0.1%
85
 
< 0.1%
155
 
< 0.1%
105
 
< 0.1%
55
 
< 0.1%
175
 
< 0.1%
Other values (25)52
 
< 0.1%
ValueCountFrequency (%)
0125686
99.8%
1151
 
0.1%
241
 
< 0.1%
35
 
< 0.1%
413
 
< 0.1%
55
 
< 0.1%
63
 
< 0.1%
74
 
< 0.1%
85
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
431
 
< 0.1%
403
< 0.1%
381
 
< 0.1%
361
 
< 0.1%
341
 
< 0.1%
331
 
< 0.1%
291
 
< 0.1%
281
 
< 0.1%
271
 
< 0.1%
263
< 0.1%

num_shells
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0.0
125926 
1.0
 
42
2.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters377919
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0125926
> 99.9%
1.042
 
< 0.1%
2.05
 
< 0.1%

Length

2023-02-19T21:08:55.494917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-19T21:08:55.566929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0125926
> 99.9%
1.042
 
< 0.1%
2.05
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0251899
66.7%
.125973
33.3%
142
 
< 0.1%
25
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number251946
66.7%
Other Punctuation125973
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0251899
> 99.9%
142
 
< 0.1%
25
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.125973
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common377919
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0251899
66.7%
.125973
33.3%
142
 
< 0.1%
25
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII377919
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0251899
66.7%
.125973
33.3%
142
 
< 0.1%
25
 
< 0.1%

num_access_files
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.004096115834
Minimum0
Maximum9
Zeros125602
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-02-19T21:08:55.633441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.09936955575
Coefficient of variation (CV)24.25945939
Kurtosis2862.804357
Mean0.004096115834
Median Absolute Deviation (MAD)0
Skewness45.55496112
Sum516
Variance0.00987430861
MonotonicityNot monotonic
2023-02-19T21:08:55.691451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0125602
99.7%
1313
 
0.2%
229
 
< 0.1%
38
 
< 0.1%
56
 
< 0.1%
45
 
< 0.1%
64
 
< 0.1%
83
 
< 0.1%
72
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
0125602
99.7%
1313
 
0.2%
229
 
< 0.1%
38
 
< 0.1%
45
 
< 0.1%
56
 
< 0.1%
64
 
< 0.1%
72
 
< 0.1%
83
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
91
 
< 0.1%
83
 
< 0.1%
72
 
< 0.1%
64
 
< 0.1%
56
 
< 0.1%
45
 
< 0.1%
38
 
< 0.1%
229
 
< 0.1%
1313
 
0.2%
0125602
99.7%

num_outbound_cmds
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0.0
125973 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters377919
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0125973
100.0%

Length

2023-02-19T21:08:55.757463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-19T21:08:55.824975image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0125973
100.0%

Most occurring characters

ValueCountFrequency (%)
0251946
66.7%
.125973
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number251946
66.7%
Other Punctuation125973
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0251946
100.0%
Other Punctuation
ValueCountFrequency (%)
.125973
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common377919
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0251946
66.7%
.125973
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII377919
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0251946
66.7%
.125973
33.3%

is_host_login
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0
125972 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125973
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0125972
> 99.9%
11
 
< 0.1%

Length

2023-02-19T21:08:55.881484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-19T21:08:55.949496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0125972
> 99.9%
11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0125972
> 99.9%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number125973
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0125972
> 99.9%
11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common125973
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0125972
> 99.9%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII125973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0125972
> 99.9%
11
 
< 0.1%

is_guest_login
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
0
124786 
1
 
1187

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125973
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0124786
99.1%
11187
 
0.9%

Length

2023-02-19T21:08:56.006006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-19T21:08:56.074018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0124786
99.1%
11187
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0124786
99.1%
11187
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number125973
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0124786
99.1%
11187
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common125973
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0124786
99.1%
11187
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII125973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0124786
99.1%
11187
 
0.9%

count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct512
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.10755479
Minimum0
Maximum511
Zeros13
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-02-19T21:08:56.139029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median14
Q3143
95-th percentile286
Maximum511
Range511
Interquartile range (IQR)141

Descriptive statistics

Standard deviation114.5086074
Coefficient of variation (CV)1.361454481
Kurtosis2.006916631
Mean84.10755479
Median Absolute Deviation (MAD)13
Skewness1.514274519
Sum10595281
Variance13112.22116
MonotonicityNot monotonic
2023-02-19T21:08:56.224544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127763
22.0%
29474
 
7.5%
33962
 
3.1%
43550
 
2.8%
52980
 
2.4%
62413
 
1.9%
72325
 
1.8%
81902
 
1.5%
91712
 
1.4%
101610
 
1.3%
Other values (502)68282
54.2%
ValueCountFrequency (%)
013
 
< 0.1%
127763
22.0%
29474
 
7.5%
33962
 
3.1%
43550
 
2.8%
52980
 
2.4%
62413
 
1.9%
72325
 
1.8%
81902
 
1.5%
91712
 
1.4%
ValueCountFrequency (%)
5111437
1.1%
510307
 
0.2%
509243
 
0.2%
50831
 
< 0.1%
5076
 
< 0.1%
5063
 
< 0.1%
5052
 
< 0.1%
5043
 
< 0.1%
5034
 
< 0.1%
5025
 
< 0.1%

srv_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct509
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.73788828
Minimum0
Maximum511
Zeros13
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-02-19T21:08:56.312059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median8
Q318
95-th percentile158
Maximum511
Range511
Interquartile range (IQR)16

Descriptive statistics

Standard deviation72.63583965
Coefficient of variation (CV)2.618650667
Kurtosis24.24448367
Mean27.73788828
Median Absolute Deviation (MAD)7
Skewness4.694161923
Sum3494225
Variance5275.965201
MonotonicityNot monotonic
2023-02-19T21:08:56.401575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
125398
20.2%
212820
 
10.2%
36336
 
5.0%
45526
 
4.4%
54636
 
3.7%
64156
 
3.3%
73992
 
3.2%
83697
 
2.9%
93528
 
2.8%
113293
 
2.6%
Other values (499)52591
41.7%
ValueCountFrequency (%)
013
 
< 0.1%
125398
20.2%
212820
10.2%
36336
 
5.0%
45526
 
4.4%
54636
 
3.7%
64156
 
3.3%
73992
 
3.2%
83697
 
2.9%
93528
 
2.8%
ValueCountFrequency (%)
5111012
0.8%
510160
 
0.1%
50949
 
< 0.1%
50811
 
< 0.1%
5073
 
< 0.1%
5031
 
< 0.1%
5022
 
< 0.1%
5011
 
< 0.1%
5002
 
< 0.1%
4992
 
< 0.1%

serror_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct89
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2844845324
Minimum0
Maximum1
Zeros86829
Zeros (%)68.9%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-02-19T21:08:56.491090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4464556243
Coefficient of variation (CV)1.569349379
Kurtosis-1.054604056
Mean0.2844845324
Median Absolute Deviation (MAD)0
Skewness0.9632005121
Sum35837.37
Variance0.1993226245
MonotonicityNot monotonic
2023-02-19T21:08:56.576105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
086829
68.9%
134439
 
27.3%
0.5493
 
0.4%
0.33321
 
0.3%
0.07305
 
0.2%
0.06298
 
0.2%
0.08254
 
0.2%
0.99250
 
0.2%
0.01216
 
0.2%
0.25208
 
0.2%
Other values (79)2360
 
1.9%
ValueCountFrequency (%)
086829
68.9%
0.01216
 
0.2%
0.0284
 
0.1%
0.03150
 
0.1%
0.04131
 
0.1%
0.05192
 
0.2%
0.06298
 
0.2%
0.07305
 
0.2%
0.08254
 
0.2%
0.09189
 
0.2%
ValueCountFrequency (%)
134439
27.3%
0.99250
 
0.2%
0.9864
 
0.1%
0.9779
 
0.1%
0.9641
 
< 0.1%
0.9529
 
< 0.1%
0.9425
 
< 0.1%
0.9318
 
< 0.1%
0.9215
 
< 0.1%
0.918
 
< 0.1%

srv_serror_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct86
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2824853738
Minimum0
Maximum1
Zeros88754
Zeros (%)70.5%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-02-19T21:08:56.666621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4470224984
Coefficient of variation (CV)1.582462456
Kurtosis-1.04429378
Mean0.2824853738
Median Absolute Deviation (MAD)0
Skewness0.9705972383
Sum35585.53
Variance0.199829114
MonotonicityNot monotonic
2023-02-19T21:08:57.831823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
088754
70.5%
134874
 
27.7%
0.5432
 
0.3%
0.33273
 
0.2%
0.25233
 
0.2%
0.2132
 
0.1%
0.17114
 
0.1%
0.0593
 
0.1%
0.0392
 
0.1%
0.0483
 
0.1%
Other values (76)893
 
0.7%
ValueCountFrequency (%)
088754
70.5%
0.016
 
< 0.1%
0.0260
 
< 0.1%
0.0392
 
0.1%
0.0483
 
0.1%
0.0593
 
0.1%
0.0665
 
0.1%
0.0767
 
0.1%
0.0863
 
0.1%
0.0944
 
< 0.1%
ValueCountFrequency (%)
134874
27.7%
0.961
 
< 0.1%
0.9540
 
< 0.1%
0.9413
 
< 0.1%
0.938
 
< 0.1%
0.9212
 
< 0.1%
0.9116
 
< 0.1%
0.910
 
< 0.1%
0.8912
 
< 0.1%
0.8811
 
< 0.1%

rerror_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct82
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1199584832
Minimum0
Maximum1
Zeros109783
Zeros (%)87.1%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-02-19T21:08:57.918839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3204355207
Coefficient of variation (CV)2.671220178
Kurtosis3.445847127
Mean0.1199584832
Median Absolute Deviation (MAD)0
Skewness2.325531602
Sum15111.53
Variance0.102678923
MonotonicityNot monotonic
2023-02-19T21:08:58.005354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0109783
87.1%
112874
 
10.2%
0.9269
 
0.2%
0.92216
 
0.2%
0.93210
 
0.2%
0.89196
 
0.2%
0.91187
 
0.1%
0.5163
 
0.1%
0.88141
 
0.1%
0.95137
 
0.1%
Other values (72)1797
 
1.4%
ValueCountFrequency (%)
0109783
87.1%
0.0161
 
< 0.1%
0.0277
 
0.1%
0.0399
 
0.1%
0.0455
 
< 0.1%
0.0537
 
< 0.1%
0.0625
 
< 0.1%
0.0723
 
< 0.1%
0.0823
 
< 0.1%
0.0910
 
< 0.1%
ValueCountFrequency (%)
112874
10.2%
0.9923
 
< 0.1%
0.9817
 
< 0.1%
0.9732
 
< 0.1%
0.9672
 
0.1%
0.95137
 
0.1%
0.94121
 
0.1%
0.93210
 
0.2%
0.92216
 
0.2%
0.91187
 
0.1%

srv_rerror_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct62
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1211832694
Minimum0
Maximum1
Zeros109767
Zeros (%)87.1%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-02-19T21:08:58.092369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.323647228
Coefficient of variation (CV)2.670725336
Kurtosis3.445834226
Mean0.1211832694
Median Absolute Deviation (MAD)0
Skewness2.327032843
Sum15265.82
Variance0.1047475282
MonotonicityNot monotonic
2023-02-19T21:08:58.178884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0109767
87.1%
114827
 
11.8%
0.5244
 
0.2%
0.33160
 
0.1%
0.25114
 
0.1%
0.292
 
0.1%
0.1773
 
0.1%
0.0450
 
< 0.1%
0.0347
 
< 0.1%
0.1445
 
< 0.1%
Other values (52)554
 
0.4%
ValueCountFrequency (%)
0109767
87.1%
0.013
 
< 0.1%
0.0240
 
< 0.1%
0.0347
 
< 0.1%
0.0450
 
< 0.1%
0.0542
 
< 0.1%
0.0633
 
< 0.1%
0.0727
 
< 0.1%
0.0840
 
< 0.1%
0.0924
 
< 0.1%
ValueCountFrequency (%)
114827
11.8%
0.962
 
< 0.1%
0.951
 
< 0.1%
0.922
 
< 0.1%
0.91
 
< 0.1%
0.893
 
< 0.1%
0.884
 
< 0.1%
0.873
 
< 0.1%
0.865
 
< 0.1%
0.8510
 
< 0.1%

same_srv_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6609276591
Minimum0
Maximum1
Zeros2766
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-02-19T21:08:58.266899image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.09
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.91

Descriptive statistics

Standard deviation0.4396228624
Coefficient of variation (CV)0.6651603339
Kurtosis-1.609765806
Mean0.6609276591
Median Absolute Deviation (MAD)0
Skewness-0.5724994801
Sum83259.04
Variance0.1932682612
MonotonicityNot monotonic
2023-02-19T21:08:58.361415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
176812
61.0%
0.014027
 
3.2%
0.023616
 
2.9%
0.033503
 
2.8%
0.073438
 
2.7%
0.043227
 
2.6%
0.063220
 
2.6%
0.053088
 
2.5%
0.082815
 
2.2%
02766
 
2.2%
Other values (91)19461
 
15.4%
ValueCountFrequency (%)
02766
2.2%
0.014027
3.2%
0.023616
2.9%
0.033503
2.8%
0.043227
2.6%
0.053088
2.5%
0.063220
2.6%
0.073438
2.7%
0.082815
2.2%
0.091957
1.6%
ValueCountFrequency (%)
176812
61.0%
0.99759
 
0.6%
0.9897
 
0.1%
0.9744
 
< 0.1%
0.9616
 
< 0.1%
0.9514
 
< 0.1%
0.9421
 
< 0.1%
0.9333
 
< 0.1%
0.9243
 
< 0.1%
0.9125
 
< 0.1%

diff_srv_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct95
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06305263826
Minimum0
Maximum1
Zeros76217
Zeros (%)60.5%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-02-19T21:08:58.454432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.06
95-th percentile0.29
Maximum1
Range1
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.1803144075
Coefficient of variation (CV)2.859744056
Kurtosis18.8994721
Mean0.06305263826
Median Absolute Deviation (MAD)0
Skewness4.379815401
Sum7942.93
Variance0.03251328556
MonotonicityNot monotonic
2023-02-19T21:08:58.543947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
076217
60.5%
0.0618998
 
15.1%
0.079515
 
7.6%
0.056887
 
5.5%
13438
 
2.7%
0.081883
 
1.5%
0.011013
 
0.8%
0.09645
 
0.5%
0.04627
 
0.5%
0.5549
 
0.4%
Other values (85)6201
 
4.9%
ValueCountFrequency (%)
076217
60.5%
0.011013
 
0.8%
0.02264
 
0.2%
0.03282
 
0.2%
0.04627
 
0.5%
0.056887
 
5.5%
0.0618998
 
15.1%
0.079515
 
7.6%
0.081883
 
1.5%
0.09645
 
0.5%
ValueCountFrequency (%)
13438
2.7%
0.9939
 
< 0.1%
0.986
 
< 0.1%
0.977
 
< 0.1%
0.9629
 
< 0.1%
0.9539
 
< 0.1%
0.922
 
< 0.1%
0.911
 
< 0.1%
0.91
 
< 0.1%
0.891
 
< 0.1%

srv_diff_host_rate
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct60
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09732164829
Minimum0
Maximum1
Zeros97574
Zeros (%)77.5%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-02-19T21:08:58.635963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2598304981
Coefficient of variation (CV)2.669811935
Kurtosis6.816307244
Mean0.09732164829
Median Absolute Deviation (MAD)0
Skewness2.860354518
Sum12259.9
Variance0.06751188775
MonotonicityNot monotonic
2023-02-19T21:08:58.722978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
097574
77.5%
18143
 
6.5%
0.012865
 
2.3%
0.5982
 
0.8%
0.67975
 
0.8%
0.12904
 
0.7%
0.33790
 
0.6%
0.02771
 
0.6%
0.11732
 
0.6%
0.25724
 
0.6%
Other values (50)11513
 
9.1%
ValueCountFrequency (%)
097574
77.5%
0.012865
 
2.3%
0.02771
 
0.6%
0.03218
 
0.2%
0.04187
 
0.1%
0.05325
 
0.3%
0.06520
 
0.4%
0.07519
 
0.4%
0.08653
 
0.5%
0.09618
 
0.5%
ValueCountFrequency (%)
18143
6.5%
0.881
 
< 0.1%
0.837
 
< 0.1%
0.860
 
< 0.1%
0.75235
 
0.2%
0.719
 
< 0.1%
0.67975
 
0.8%
0.627
 
< 0.1%
0.6178
 
0.1%
0.5733
 
< 0.1%

dst_host_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct256
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean182.1489446
Minimum0
Maximum255
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-02-19T21:08:58.809493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q182
median255
Q3255
95-th percentile255
Maximum255
Range255
Interquartile range (IQR)173

Descriptive statistics

Standard deviation99.20621303
Coefficient of variation (CV)0.544643359
Kurtosis-1.065772798
Mean182.1489446
Median Absolute Deviation (MAD)0
Skewness-0.8334376779
Sum22945849
Variance9841.872705
MonotonicityNot monotonic
2023-02-19T21:08:58.898009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25574099
58.8%
13119
 
2.5%
22733
 
2.2%
31280
 
1.0%
41198
 
1.0%
5723
 
0.6%
6701
 
0.6%
7645
 
0.5%
8595
 
0.5%
9578
 
0.5%
Other values (246)40302
32.0%
ValueCountFrequency (%)
03
 
< 0.1%
13119
2.5%
22733
2.2%
31280
1.0%
41198
 
1.0%
5723
 
0.6%
6701
 
0.6%
7645
 
0.5%
8595
 
0.5%
9578
 
0.5%
ValueCountFrequency (%)
25574099
58.8%
25470
 
0.1%
25389
 
0.1%
25277
 
0.1%
25190
 
0.1%
25093
 
0.1%
24978
 
0.1%
24887
 
0.1%
24789
 
0.1%
24683
 
0.1%

dst_host_srv_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct256
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.653005
Minimum0
Maximum255
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-02-19T21:08:58.984524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q110
median63
Q3255
95-th percentile255
Maximum255
Range255
Interquartile range (IQR)245

Descriptive statistics

Standard deviation110.7027408
Coefficient of variation (CV)0.957197271
Kurtosis-1.756334887
Mean115.653005
Median Absolute Deviation (MAD)61
Skewness0.2837211875
Sum14569156
Variance12255.09682
MonotonicityNot monotonic
2023-02-19T21:08:59.074039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25535993
28.6%
18449
 
6.7%
25161
 
4.1%
32768
 
2.2%
42488
 
2.0%
52336
 
1.9%
202300
 
1.8%
2542238
 
1.8%
62222
 
1.8%
192190
 
1.7%
Other values (246)59828
47.5%
ValueCountFrequency (%)
03
 
< 0.1%
18449
6.7%
25161
4.1%
32768
 
2.2%
42488
 
2.0%
52336
 
1.9%
62222
 
1.8%
72160
 
1.7%
82072
 
1.6%
91948
 
1.5%
ValueCountFrequency (%)
25535993
28.6%
2542238
 
1.8%
253472
 
0.4%
252213
 
0.2%
251402
 
0.3%
250302
 
0.2%
249248
 
0.2%
248205
 
0.2%
247220
 
0.2%
246196
 
0.2%

dst_host_same_srv_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5212416946
Minimum0
Maximum1
Zeros6927
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-02-19T21:08:59.162555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.05
median0.51
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.95

Descriptive statistics

Standard deviation0.4489493637
Coefficient of variation (CV)0.861307467
Kurtosis-1.884045869
Mean0.5212416946
Median Absolute Deviation (MAD)0.49
Skewness-0.01044802066
Sum65662.38
Variance0.2015555312
MonotonicityNot monotonic
2023-02-19T21:08:59.251070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
149059
38.9%
0.017780
 
6.2%
06927
 
5.5%
0.026593
 
5.2%
0.075672
 
4.5%
0.045208
 
4.1%
0.054951
 
3.9%
0.034049
 
3.2%
0.063444
 
2.7%
0.082816
 
2.2%
Other values (91)29474
23.4%
ValueCountFrequency (%)
06927
5.5%
0.017780
6.2%
0.026593
5.2%
0.034049
3.2%
0.045208
4.1%
0.054951
3.9%
0.063444
2.7%
0.075672
4.5%
0.082816
 
2.2%
0.091740
 
1.4%
ValueCountFrequency (%)
149059
38.9%
0.99688
 
0.5%
0.98821
 
0.7%
0.97478
 
0.4%
0.96675
 
0.5%
0.95580
 
0.5%
0.94393
 
0.3%
0.93457
 
0.4%
0.92341
 
0.3%
0.91395
 
0.3%

dst_host_diff_srv_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08295110857
Minimum0
Maximum1
Zeros46989
Zeros (%)37.3%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-02-19T21:08:59.341586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.02
Q30.07
95-th percentile0.56
Maximum1
Range1
Interquartile range (IQR)0.07

Descriptive statistics

Standard deviation0.1889217999
Coefficient of variation (CV)2.277507838
Kurtosis12.63440958
Mean0.08295110857
Median Absolute Deviation (MAD)0.02
Skewness3.609600381
Sum10449.6
Variance0.03569144648
MonotonicityNot monotonic
2023-02-19T21:08:59.428601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
046989
37.3%
0.0716570
 
13.2%
0.069787
 
7.8%
0.019295
 
7.4%
0.057321
 
5.8%
0.087001
 
5.6%
0.026716
 
5.3%
0.033563
 
2.8%
0.043091
 
2.5%
0.092569
 
2.0%
Other values (91)13071
 
10.4%
ValueCountFrequency (%)
046989
37.3%
0.019295
 
7.4%
0.026716
 
5.3%
0.033563
 
2.8%
0.043091
 
2.5%
0.057321
 
5.8%
0.069787
 
7.8%
0.0716570
 
13.2%
0.087001
 
5.6%
0.092569
 
2.0%
ValueCountFrequency (%)
12139
1.7%
0.9931
 
< 0.1%
0.9835
 
< 0.1%
0.9786
 
0.1%
0.9663
 
0.1%
0.9587
 
0.1%
0.9445
 
< 0.1%
0.9354
 
< 0.1%
0.9240
 
< 0.1%
0.9186
 
0.1%

dst_host_same_src_port_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.148378859
Minimum0
Maximum1
Zeros63023
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-02-19T21:08:59.519117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.06
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.3089971304
Coefficient of variation (CV)2.082487576
Kurtosis2.762402078
Mean0.148378859
Median Absolute Deviation (MAD)0
Skewness2.087039441
Sum18691.73
Variance0.09547922658
MonotonicityNot monotonic
2023-02-19T21:08:59.607632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
063023
50.0%
0.0117657
 
14.0%
110307
 
8.2%
0.025743
 
4.6%
0.033278
 
2.6%
0.042096
 
1.7%
0.051664
 
1.3%
0.061299
 
1.0%
0.081086
 
0.9%
0.51077
 
0.9%
Other values (91)18743
 
14.9%
ValueCountFrequency (%)
063023
50.0%
0.0117657
 
14.0%
0.025743
 
4.6%
0.033278
 
2.6%
0.042096
 
1.7%
0.051664
 
1.3%
0.061299
 
1.0%
0.071051
 
0.8%
0.081086
 
0.9%
0.09712
 
0.6%
ValueCountFrequency (%)
110307
8.2%
0.99139
 
0.1%
0.98192
 
0.2%
0.97145
 
0.1%
0.96229
 
0.2%
0.95220
 
0.2%
0.94113
 
0.1%
0.93159
 
0.1%
0.92124
 
0.1%
0.91149
 
0.1%

dst_host_srv_diff_host_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct75
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03254244957
Minimum0
Maximum1
Zeros86904
Zeros (%)69.0%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-02-19T21:08:59.698148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.02
95-th percentile0.18
Maximum1
Range1
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.1125638049
Coefficient of variation (CV)3.458983769
Kurtosis35.77323559
Mean0.03254244957
Median Absolute Deviation (MAD)0
Skewness5.548174497
Sum4099.47
Variance0.01267061017
MonotonicityNot monotonic
2023-02-19T21:08:59.785163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
086904
69.0%
0.027952
 
6.3%
0.017146
 
5.7%
0.034723
 
3.7%
0.044518
 
3.6%
0.053048
 
2.4%
0.51550
 
1.2%
0.061330
 
1.1%
0.071036
 
0.8%
0.25951
 
0.8%
Other values (65)6815
 
5.4%
ValueCountFrequency (%)
086904
69.0%
0.017146
 
5.7%
0.027952
 
6.3%
0.034723
 
3.7%
0.044518
 
3.6%
0.053048
 
2.4%
0.061330
 
1.1%
0.071036
 
0.8%
0.08488
 
0.4%
0.09414
 
0.3%
ValueCountFrequency (%)
1691
0.5%
0.972
 
< 0.1%
0.931
 
< 0.1%
0.881
 
< 0.1%
0.862
 
< 0.1%
0.832
 
< 0.1%
0.84
 
< 0.1%
0.781
 
< 0.1%
0.7517
 
< 0.1%
0.732
 
< 0.1%

dst_host_serror_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.284452462
Minimum0
Maximum1
Zeros81386
Zeros (%)64.6%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-02-19T21:08:59.874178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4447840503
Coefficient of variation (CV)1.563649853
Kurtosis-1.046995556
Mean0.284452462
Median Absolute Deviation (MAD)0
Skewness0.9659523157
Sum35833.33
Variance0.1978328514
MonotonicityNot monotonic
2023-02-19T21:08:59.962694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
081386
64.6%
133562
26.6%
0.013345
 
2.7%
0.021158
 
0.9%
0.03711
 
0.6%
0.09419
 
0.3%
0.08413
 
0.3%
0.04372
 
0.3%
0.99304
 
0.2%
0.05298
 
0.2%
Other values (91)4005
 
3.2%
ValueCountFrequency (%)
081386
64.6%
0.013345
 
2.7%
0.021158
 
0.9%
0.03711
 
0.6%
0.04372
 
0.3%
0.05298
 
0.2%
0.06174
 
0.1%
0.07197
 
0.2%
0.08413
 
0.3%
0.09419
 
0.3%
ValueCountFrequency (%)
133562
26.6%
0.99304
 
0.2%
0.98169
 
0.1%
0.97100
 
0.1%
0.96102
 
0.1%
0.9571
 
0.1%
0.9487
 
0.1%
0.9376
 
0.1%
0.9253
 
< 0.1%
0.9148
 
< 0.1%

dst_host_srv_serror_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct100
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2784845165
Minimum0
Maximum1
Zeros85360
Zeros (%)67.8%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-02-19T21:09:00.057711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4456691239
Coefficient of variation (CV)1.600337173
Kurtosis-1.007991978
Mean0.2784845165
Median Absolute Deviation (MAD)0
Skewness0.9917336307
Sum35081.53
Variance0.198620968
MonotonicityNot monotonic
2023-02-19T21:09:00.144726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
085360
67.8%
134256
27.2%
0.013762
 
3.0%
0.02640
 
0.5%
0.03160
 
0.1%
0.04111
 
0.1%
0.5107
 
0.1%
0.0576
 
0.1%
0.0872
 
0.1%
0.0771
 
0.1%
Other values (90)1358
 
1.1%
ValueCountFrequency (%)
085360
67.8%
0.013762
 
3.0%
0.02640
 
0.5%
0.03160
 
0.1%
0.04111
 
0.1%
0.0576
 
0.1%
0.0653
 
< 0.1%
0.0771
 
0.1%
0.0872
 
0.1%
0.0957
 
< 0.1%
ValueCountFrequency (%)
134256
27.2%
0.9853
 
< 0.1%
0.9756
 
< 0.1%
0.9644
 
< 0.1%
0.9526
 
< 0.1%
0.9422
 
< 0.1%
0.9320
 
< 0.1%
0.9226
 
< 0.1%
0.9120
 
< 0.1%
0.915
 
< 0.1%

dst_host_rerror_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1188318132
Minimum0
Maximum1
Zeros103178
Zeros (%)81.9%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-02-19T21:09:00.232241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.306557458
Coefficient of variation (CV)2.579759156
Kurtosis3.69274866
Mean0.1188318132
Median Absolute Deviation (MAD)0
Skewness2.347445837
Sum14969.6
Variance0.09397747507
MonotonicityNot monotonic
2023-02-19T21:09:00.324257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0103178
81.9%
110298
 
8.2%
0.011800
 
1.4%
0.021222
 
1.0%
0.03497
 
0.4%
0.05409
 
0.3%
0.04397
 
0.3%
0.91267
 
0.2%
0.92257
 
0.2%
0.89244
 
0.2%
Other values (91)7404
 
5.9%
ValueCountFrequency (%)
0103178
81.9%
0.011800
 
1.4%
0.021222
 
1.0%
0.03497
 
0.4%
0.04397
 
0.3%
0.05409
 
0.3%
0.06220
 
0.2%
0.07164
 
0.1%
0.08147
 
0.1%
0.09104
 
0.1%
ValueCountFrequency (%)
110298
8.2%
0.9952
 
< 0.1%
0.9868
 
0.1%
0.97106
 
0.1%
0.96168
 
0.1%
0.95123
 
0.1%
0.94135
 
0.1%
0.93111
 
0.1%
0.92257
 
0.2%
0.91267
 
0.2%

dst_host_srv_rerror_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1202398927
Minimum0
Maximum1
Zeros106616
Zeros (%)84.6%
Negative0
Negative (%)0.0%
Memory size984.3 KiB
2023-02-19T21:09:00.415272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3194593905
Coefficient of variation (CV)2.656850263
Kurtosis3.520661878
Mean0.1202398927
Median Absolute Deviation (MAD)0
Skewness2.337926283
Sum15146.98
Variance0.1020543022
MonotonicityNot monotonic
2023-02-19T21:09:00.504288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0106616
84.6%
113231
 
10.5%
0.011390
 
1.1%
0.02580
 
0.5%
0.03352
 
0.3%
0.05351
 
0.3%
0.04344
 
0.3%
0.98189
 
0.2%
0.99188
 
0.1%
0.06185
 
0.1%
Other values (91)2547
 
2.0%
ValueCountFrequency (%)
0106616
84.6%
0.011390
 
1.1%
0.02580
 
0.5%
0.03352
 
0.3%
0.04344
 
0.3%
0.05351
 
0.3%
0.06185
 
0.1%
0.0797
 
0.1%
0.0866
 
0.1%
0.0939
 
< 0.1%
ValueCountFrequency (%)
113231
10.5%
0.99188
 
0.1%
0.98189
 
0.2%
0.97103
 
0.1%
0.9678
 
0.1%
0.9573
 
0.1%
0.9475
 
0.1%
0.9350
 
< 0.1%
0.9238
 
< 0.1%
0.9151
 
< 0.1%

class
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size984.3 KiB
normal
67343 
anomaly
58630 

Length

Max length7
Median length6
Mean length6.465417193
Min length6

Characters and Unicode

Total characters814468
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rownormal
3rd rowanomaly
4th rownormal
5th rownormal

Common Values

ValueCountFrequency (%)
normal67343
53.5%
anomaly58630
46.5%

Length

2023-02-19T21:09:00.588803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-19T21:09:00.660815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
normal67343
53.5%
anomaly58630
46.5%

Most occurring characters

ValueCountFrequency (%)
a184603
22.7%
n125973
15.5%
o125973
15.5%
m125973
15.5%
l125973
15.5%
r67343
 
8.3%
y58630
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter814468
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a184603
22.7%
n125973
15.5%
o125973
15.5%
m125973
15.5%
l125973
15.5%
r67343
 
8.3%
y58630
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
Latin814468
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a184603
22.7%
n125973
15.5%
o125973
15.5%
m125973
15.5%
l125973
15.5%
r67343
 
8.3%
y58630
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII814468
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a184603
22.7%
n125973
15.5%
o125973
15.5%
m125973
15.5%
l125973
15.5%
r67343
 
8.3%
y58630
 
7.2%

Interactions

2023-02-19T21:08:47.840565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:20.492912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:23.579449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:26.820587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:29.955132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:36.806305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:39.984355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:43.249928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:46.213871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:49.582957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:52.335020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:55.331041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:58.763568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:01.578668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:04.370482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:07.347728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:10.679808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:13.564309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:16.469814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:19.365322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:22.945448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:25.901961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:28.891853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:31.938893image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:34.900908image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:38.957114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:42.011645image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:44.854046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:47.949084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:20.644439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:23.688468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:26.925105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:30.062150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:36.902818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:40.085873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:43.347945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:46.309889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:49.675473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:52.446039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:55.436559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:58.859085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:01.674685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:04.465498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:07.460747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:10.777825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:13.661326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:16.568831image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:19.468339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:23.046464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:26.001979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:28.994870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:32.035910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:35.015929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:39.055631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:42.104661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:44.949062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:48.068105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:20.769961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:23.801487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:27.033124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:30.173170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:37.010337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:40.203392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:43.453968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:46.415907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:49.780072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:52.556059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:55.548079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:58.963603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:01.780203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:04.571517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:07.565265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:10.884844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:13.767344image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:16.679351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:19.584360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:23.154484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:26.110498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:29.110390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:32.142929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:35.125947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:39.160649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:42.207679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:45.057081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:48.206629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:20.888481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:23.915008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:27.141643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:30.283189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:37.119356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:40.312912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:43.560486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:46.517924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:49.881589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:52.663077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:55.658098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:59.065120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:01.883720image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:04.672534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:07.664783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:10.988362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:13.871863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:16.784370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:19.690379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:23.263502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:26.217016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-02-19T21:07:39.623792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:42.929873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:45.882314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:49.287406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:52.037468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:55.012986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:58.456015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:01.277616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:04.070930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:06.997167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:10.380256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:13.253755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:16.149259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:19.058270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:22.636390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:25.587907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:28.564795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:31.618338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:34.584353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:37.755405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:41.709593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:44.547493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:47.467000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:50.914600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:23.361911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:26.595473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:29.740094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:36.580764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:39.725810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:43.033891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:45.990333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:49.386923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:52.134986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:55.121005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:58.561533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:01.376633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:04.171447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:07.114687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:10.478773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:13.356774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:16.255277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:19.159786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:22.737407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:25.692925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:28.675815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:31.730357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:34.690372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:38.759579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:41.813111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:44.651010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:47.599524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:51.013617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:23.466929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:26.706493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:29.850614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:36.699783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:39.878836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:43.136908image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:46.100352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:49.482440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:52.230502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:55.227023image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:07:58.665051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:01.479651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:04.271464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:07.231207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:10.577790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:13.462292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:16.362295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:19.260804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:22.839929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:25.799444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:28.783333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:31.836875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:34.795889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:38.856597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:41.914128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:44.753529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-02-19T21:08:47.718044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-02-19T21:09:00.747360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-02-19T21:09:00.987902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-02-19T21:09:01.230444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-02-19T21:09:01.450983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2023-02-19T21:09:01.601509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-02-19T21:08:51.264665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-19T21:08:52.318848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

durationprotocol_typeserviceflagsrc_bytesdst_byteslandwrong_fragmenturgenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesnum_outbound_cmdsis_host_loginis_guest_logincountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_rateclass
00.0tcpftp_dataSF491.00.000.00.00.00.000.00.00.00.00.00.00.00.0002.02.00.00.00.00.01.000.000.00150.025.00.170.030.170.000.000.000.050.00normal
10.0udpotherSF146.00.000.00.00.00.000.00.00.00.00.00.00.00.00013.01.00.00.00.00.00.080.150.00255.01.00.000.600.880.000.000.000.000.00normal
20.0tcpprivateS00.00.000.00.00.00.000.00.00.00.00.00.00.00.000123.06.01.01.00.00.00.050.070.00255.026.00.100.050.000.001.001.000.000.00anomaly
30.0tcphttpSF232.08153.000.00.00.00.010.00.00.00.00.00.00.00.0005.05.00.20.20.00.01.000.000.0030.0255.01.000.000.030.040.030.010.000.01normal
40.0tcphttpSF199.0420.000.00.00.00.010.00.00.00.00.00.00.00.00030.032.00.00.00.00.01.000.000.09255.0255.01.000.000.000.000.000.000.000.00normal
50.0tcpprivateREJ0.00.000.00.00.00.000.00.00.00.00.00.00.00.000121.019.00.00.01.01.00.160.060.00255.019.00.070.070.000.000.000.001.001.00anomaly
60.0tcpprivateS00.00.000.00.00.00.000.00.00.00.00.00.00.00.000166.09.01.01.00.00.00.050.060.00255.09.00.040.050.000.001.001.000.000.00anomaly
70.0tcpprivateS00.00.000.00.00.00.000.00.00.00.00.00.00.00.000117.016.01.01.00.00.00.140.060.00255.015.00.060.070.000.001.001.000.000.00anomaly
80.0tcpremote_jobS00.00.000.00.00.00.000.00.00.00.00.00.00.00.000270.023.01.01.00.00.00.090.050.00255.023.00.090.050.000.001.001.000.000.00anomaly
90.0tcpprivateS00.00.000.00.00.00.000.00.00.00.00.00.00.00.000133.08.01.01.00.00.00.060.060.00255.013.00.050.060.000.001.001.000.000.00anomaly

Last rows

durationprotocol_typeserviceflagsrc_bytesdst_byteslandwrong_fragmenturgenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesnum_outbound_cmdsis_host_loginis_guest_logincountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_rateclass
1259630.0tcphttpSF334.01600.000.00.00.00.010.00.00.00.00.00.00.00.0003.03.00.000.000.00.01.000.000.00255.0255.01.000.000.000.000.000.00.000.0normal
1259640.0tcpprivateS00.00.000.00.00.00.000.00.00.00.00.00.00.00.000128.09.01.001.000.00.00.070.050.00255.012.00.050.060.000.001.001.00.000.0anomaly
1259650.0tcpsmtpSF2233.0365.000.00.00.00.010.00.00.00.00.00.00.00.0001.01.00.000.000.00.01.000.000.001.02.01.000.001.001.000.000.00.000.0normal
1259660.0tcpprivateS00.00.000.00.00.00.000.00.00.00.00.00.00.00.000113.03.01.001.000.00.00.030.070.00255.013.00.050.070.000.001.001.00.000.0anomaly
1259670.0tcphttpSF359.0375.000.00.00.00.010.00.00.00.00.00.00.00.0003.011.00.330.090.00.01.000.000.183.0255.01.000.000.330.040.330.00.000.0normal
1259680.0tcpprivateS00.00.000.00.00.00.000.00.00.00.00.00.00.00.000184.025.01.001.000.00.00.140.060.00255.025.00.100.060.000.001.001.00.000.0anomaly
1259698.0udpprivateSF105.0145.000.00.00.00.000.00.00.00.00.00.00.00.0002.02.00.000.000.00.01.000.000.00255.0244.00.960.010.010.000.000.00.000.0normal
1259700.0tcpsmtpSF2231.0384.000.00.00.00.010.00.00.00.00.00.00.00.0001.01.00.000.000.00.01.000.000.00255.030.00.120.060.000.000.720.00.010.0normal
1259710.0tcpkloginS00.00.000.00.00.00.000.00.00.00.00.00.00.00.000144.08.01.001.000.00.00.060.050.00255.08.00.030.050.000.001.001.00.000.0anomaly
1259720.0tcpftp_dataSF151.00.000.00.00.00.010.00.00.00.00.00.00.00.0001.01.00.000.000.00.01.000.000.00255.077.00.300.030.300.000.000.00.000.0normal

Duplicate rows

Most frequently occurring

durationprotocol_typeserviceflagsrc_bytesdst_byteslandwrong_fragmenturgenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesnum_outbound_cmdsis_host_loginis_guest_logincountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_rateclass# duplicates
40.0icmpecr_iSF8.00.000.00.00.00.000.00.00.00.00.00.00.00.0001.01.00.00.00.00.01.00.00.01.01.01.00.01.00.00.00.00.00.0anomaly3
00.0icmpeco_iSF8.00.000.00.00.00.000.00.00.00.00.00.00.00.0001.01.00.00.00.00.01.00.00.01.01.01.00.01.00.00.00.00.00.0anomaly2
10.0icmpeco_iSF8.00.000.00.00.00.000.00.00.00.00.00.00.00.0001.02.00.00.00.00.01.00.01.02.02.01.00.01.00.00.00.00.00.0anomaly2
20.0icmpeco_iSF8.00.000.00.00.00.000.00.00.00.00.00.00.00.0001.07.00.00.00.00.01.00.01.02.02.01.00.01.00.00.00.00.00.0anomaly2
30.0icmpeco_iSF8.00.000.00.00.00.000.00.00.00.00.00.00.00.0001.045.00.00.00.00.01.00.01.01.01.01.00.01.00.00.00.00.00.0anomaly2
50.0icmpecr_iSF8.00.000.00.00.00.000.00.00.00.00.00.00.00.0001.01.00.00.00.00.01.00.00.03.03.01.00.01.00.00.00.00.00.0anomaly2
60.0icmpecr_iSF8.00.000.00.00.00.000.00.00.00.00.00.00.00.0001.01.00.00.00.00.01.00.00.05.05.01.00.01.00.00.00.00.00.0anomaly2
70.0icmpecr_iSF8.00.000.00.00.00.000.00.00.00.00.00.00.00.0002.02.00.00.00.00.01.00.00.02.02.01.00.01.00.00.00.00.00.0anomaly2